Current approaches to characterize agricultural field variability involve two basic processes. First is the creation of layers that describe variability of soil or crop parameters and then secondly to divide this characterized layer into classes. For example, aerial images can be used characterize parameters describing crop or soil variability. This is in addition to in-field sensors, crop color sensors, soil EM readings or other methods. Current methods for characterizing aerial images involve converting the original multi-spectral data to vegetation indices such as Normalized Difference Vegetation Index (NDVI). This NDVI layer is then divided into classes using an approach such as an equal area or equal increment approach. When divided using equal area, all fields have an equivalent distribution of high and low zones, independent of the level of actual variability, sometimes resulting in prescriptions that may be poorly related to actual crop conditions. When divided on the basis of equal increments (e.g. 31-40, 41-50 . . . ), some classes have little or no representation in some fields. In both cases, it is difficult to compare fields based upon the resultant classification. This problem is exacerbated when images, or other data, are acquired by sensors that may provide different outputs for the same crop situation depending upon conditions such as sensor settings, light level, angles of the images relative to the crops, direction of the planted rows, or differences in crop architecture, such as upright or droopy leaves.
It is known that the normalization of an image layer or sensor values yields a somewhat more consistent relationship to parameters of interest, such as yield and Soil and Plant Analysis Development (SPAD) chlorophyll. A normalized SPAD, based upon the mean pixel value from a high nitrogen reference strip, is a better indicator of nitrogen stress, or yield, than SPAD values alone. The use of relative SPAD and relative yield, based upon the highest or highest mean yield of the field, allows a more consistent relationship when more than one field is used in the analysis. Similar results have been shown for images or sensor results in addition to SPAD values. One of the problems in the prior art is the use of high nitrogen reference strips in that SPAD/pixel/sensor readings often show considerable variability within and between high nitrogen reference strips within the whole field. This makes it difficult to select a value to be used for normalization. A further problem with this approach is that growers are not eager to create high nitrogen reference strips.
What is needed in the art is the ability to select a target or best representative pixel value without the need to establish a high nitrogen reference strip.